{"title":"Top-k node identification method based on Gaussian plume model","authors":"Xu Cao, F. Yin","doi":"10.1145/3579654.3579748","DOIUrl":null,"url":null,"abstract":"As one of the most commonly used models in the Top-k node recognition task, the greedy model has the advantages of convenience, easy understanding and stable effect. The CELF++ algorithm, as a method of using the greedy strategy, also has the above characteristics. However, since the algorithm uses Monte Carlo simulation to calculate the effect of node influence diffusion, its time overhead is unbearable on large networks. Regarding the above points, this paper introduces a Gaussian plume model commonly used in the field of atmospheric pollution diffusion simulation, and proposes a Gaussian influence diffusion model. On this basis, the CELF++ algorithm is improved, and the Gaussian influence diffusion model is used to replace the traditional Monte Carlo simulation to model the influence diffusion in social networks, and the GPM-CELF++ (Gaussian Plume Model-CELF++) algorithm is proposed. Extensive experimental results on real datasets show that the proposed algorithm has advantages in both propagation effect and running time compared with baseline methods.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"74 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3579654.3579748","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
As one of the most commonly used models in the Top-k node recognition task, the greedy model has the advantages of convenience, easy understanding and stable effect. The CELF++ algorithm, as a method of using the greedy strategy, also has the above characteristics. However, since the algorithm uses Monte Carlo simulation to calculate the effect of node influence diffusion, its time overhead is unbearable on large networks. Regarding the above points, this paper introduces a Gaussian plume model commonly used in the field of atmospheric pollution diffusion simulation, and proposes a Gaussian influence diffusion model. On this basis, the CELF++ algorithm is improved, and the Gaussian influence diffusion model is used to replace the traditional Monte Carlo simulation to model the influence diffusion in social networks, and the GPM-CELF++ (Gaussian Plume Model-CELF++) algorithm is proposed. Extensive experimental results on real datasets show that the proposed algorithm has advantages in both propagation effect and running time compared with baseline methods.